Quantum Algorithms for Quantum Chemistry based on the sparsity of the CI-matrix

TL;DR

This paper introduces a quantum simulation method for quantum chemistry that leverages the sparsity of the CI-matrix, reducing qubit requirements and improving gate scaling for high-accuracy calculations.

Contribution

It applies sparse Hamiltonian simulation techniques to the CI-matrix, exploiting its structure to enhance quantum simulation efficiency in quantum chemistry.

Findings

Reduced qubit requirements for wavefunction representation

Improved gate scaling with increasing basis set size

Potential for more efficient high-accuracy quantum chemistry simulations

Abstract

Quantum chemistry provides a target for quantum simulation of considerable scientific interest and industrial importance. The majority of algorithms to date have been based on a second-quantized representation of the electronic structure Hamiltonian - necessitating qubit requirements that scale linearly with the number of orbitals. The scaling of the number of gates for such methods, while polynomial, presents some serious experimental challenges. However, because the number of electrons is a good quantum number for the electronic structure problem it is unnecessary to store the full Fock space of the orbitals. Representation of the wave function in a basis of Slater determinants for fixed electron number suffices. However, to date techniques for the quantum simulation of the Hamiltonian represented in this basis - the CI-matrix - have been lacking. We show how to apply techniques developed for the simulation of sparse Hamiltonians to the CI-matrix. We prove a number of results exploiting the structure of the CI-matrix, arising from the Slater rules which define it, to improve the application of sparse Hamiltonian simulation techniques in this case. We show that it is possible to use the minimal number of qubits to represent the wavefunction, and that these methods can offer improved scaling in the number of gates required in the limit of fixed electron number and increasing basis set size relevant for high-accuracy calculations. We hope these results open the door to further investigation of sparse Hamiltonian simulation techniques in the context of the quantum simulation of quantum chemistry.